GPU-Accelerated Human Motion Tracking Using Particle Filter Combined with PSO

  • Boguslaw Rymut
  • Bogdan Kwolek
  • Tomasz Krzeszowski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


This paper discusses how to combine particle filter (PF) with particle swarm optimization (PSO) to achieve better object tracking. Owing to multi-swarm based mode seeking the algorithm is capable of maintaining multimodal probability distributions and the tracking accuracy is far better than accuracy of PF or PSO. We propose parallel resampling scheme for particle filtering running on GPU. We show the efficiency of the parallel PF-PSO algorithm on 3D model based human motion tracking. The 3D model is rasterized in parallel and single thread processes one column of the image. Such level of parallelism allows us to efficiently utilize the GPU resources and to perform tracking of the full human body at rates of 15 frames per second. The GPU achieves an average speedup of 7.5 over the CPU. For marker-less motion capture system consisting of four calibrated cameras, the computations were conducted on four CPU cores and four GTX GPUs on two cards.


Particle Swarm Optimization Shared Memory Particle Filter Particle Swarm Optimization Algorithm Global Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Boguslaw Rymut
    • 2
  • Bogdan Kwolek
    • 1
  • Tomasz Krzeszowski
    • 2
  1. 1.AGH University of Science and TechnologyKrakówPoland
  2. 2.Rzeszów University of TechnologyRzeszówPoland

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